For evaluating our SSL technique and comparing it with the other SSL algorithms, we follow the concrete suggestions and criteria which are provided in [1]}. Some of these recommendations are as follows: 1) we use a common CNN architecture and training procedure to conduct a comparative analysis, because differences in CNN
architecture or even implementation details can influence the results. 2) We report the performance of a fully-supervised case as a baseline because the goal of SSL is to greatly outperform the fully-supervised settings. 3) We change the amount of labeled and unlabeled data when reporting the performance of our SSL algorithm because an ideal SSL method should remain efficient even with the small amount of labeled and additional unlabeled data. 4) We also perform an analysis on realistic small validation sets. This is because, in real-world applications, the large validation set is instead used as the training, therefore, an SSL algorithm which needs heavy tuning on a per-task or per-model basis to perform well would not be applicable if the validation sets are realistically small (This analysis is done in Appendix F).
